| Unit name | Visual Analytics |
|---|---|
| Unit code | EMATM0066 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. O'Grady |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
Software Development: Programming and Algorithms (EMAT0048) or Statistical Computation and Empirical Methods (EMATM0061). |
| Units you must take alongside this one (co-requisite units) |
Introduction to AI and Text Analytics |
| Units you may not take alongside this one |
None. |
| School/department | School of Engineering Mathematics and Technology |
| Faculty | Faculty of Engineering |
Why is this unit important?
Visual analytics couples the visual representation of data with analytical processes to support complex decision making and understanding. A picture may be worth a thousand words, but only if it is well designed to represent data faithfully and meaningfully. This unit will enable students to create powerful analyses of data and communicate them effectively to non-specialists.
How does this unit fit into your programme of study?
The unit complements the rest of the programme by providing a different set of tools for analysing data and presenting results. In addition, it is the culmination of the machine learning elements of the programme – techniques that are related to visual analytics but also with applications in many other aspects of data science.
None.
Tasks which help you learn and prepare you for summative tasks (formative):
All practical labs have some formative assessment – embedded questions based on the lab exercises with answers given later on the VLE.
Lectorials provide examples and case studies that will be worked through in class: students are expected to use the solutions to these to improve their understanding.
Tasks which count towards your unit mark (summative):
Four in-class quizzes that are based on material covered in the practical lab classes related to the machine learning topics (20%): ILO2 and ILO3.
Coursework: Create a visualisation of key features and a data projection of a medium-sized real-world dataset, analyse and evaluate the representation through a user trial, and report on conclusions relating them to the theory of information visualization (80%): ILO1, ILO2, ILO4.
When assessment does not go to plan
A modified version of the coursework that covers all the ILOs will be set for completion after the end of TB2 in the reassessment period.
An overview of content
This unit covers two key aspects of visual analytics: the science of information visualisation (primarily concerned with the way that data is represented visually), and advanced machine learning (as a tool to change the data representation, e.g. through dimensionality reduction, or as a way of analysing visual data) in a framework of statistical pattern recognition.
Information visualisation topics covered by this unit include: data types and their representations, non-vectorial data, human requirements for visual analytics, scientific visualisation, visualisation quality metrics, Shneiderman’s mantra (overview first, zoom and filter, details on demand) practical visualisation tools.
Machine learning topics covered by this unit include: principles of Statistical Pattern Recognition (probabilistic models for data, curse of dimensionality, generalisation error, bias-variance dilemma); linear models (Probabilistic Principal Component Analysis; Discriminant Analysis); generalised dissimilarity mappings and neighbour embedding techniques; Gaussian Processes; latent variable models (Gaussian Mixture Models, Generative Topographic Mapping and Gaussian Process Latent Variable Model); Bayesian model regularisation and combination; feature selection.
How will students, personally, be different as a result of the unit
Throughout the unit there is a focus on students understanding theory and modelling principles in order to apply them effectively to represent data. Students should be able to apply these principles to many other models and tasks in data science.
Learning Outcomes
At the end of the unit, students will be able to:
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. EMATM0066).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
